Equity in health is one of the basic values that guide the Pan American Health
Organization’s technical cooperation with the countries of the American Region.
The fundamental difference between inequities and inequalities resides in
the fact that inequities represent inequalities that are considered and qualified
as unjust and avoidable. As a result, measuring health inequalities represents
the first step towards the identification of inequities in health. In the
Region of the Americas, the availability of health information aggregated
by geographical units generally permits the analysis of inequalities, which
should serve as a basis for decision-making. Indeed, 21 countries of the Region
already dispose of data at the subnational level within the Core Data Initiative.
Carrying out these analyses is essential to reducing the inequities that are
characteristic of the health profile of the Region.

There exists a wide variety of summary measures for the magnitude of inequalities
in health. One specific indicator is the Gini Coefficient, which, along with
the Concentration Index, has been taken from the field of economics and applied
to the study of health inequalities.

Gini Coefficient and Lorenz CurveThe Gini coefficient is based on the Lorenz curve, a cumulative frequency
curve that compares the distribution of a specific variable with the uniform
distribution that represents equality (Figure 1). This equality distribution
is represented by a diagonal line, and the greater the deviation of the Lorenz
curve from this line, the greater the inequality.

Figure 1: Areas for calculation of the Gini Coefficient

When applying this index to health variables, the cumulative proportion of
the population is generally shown on the X axis, and the cumulative proportion
of the health variable on the Y axis. The greater the distance from the diagonal
line, the greater the inequality. The curve can be below or above the diagonal
depending on the variable used. When the variable is beneficial to the population,
as for example in the case of access to water, the curve is found below the
diagonal line. In contrast, when the variable is prejudicial, as in the case
of deaths, it is found above the line.

The Gini Coefficient ranges from 0 to 1, 0 representing perfect equality
and 1 total inequality. It corresponds to twice the area between the Lorenz
curve and the diagonal (Figure 1). There are different methods to calculate
the Gini, but a simple formula, shown below, was provided by Brown
(1994).

The first step for calculating the Gini coefficient using geopolitically
aggregated data is to sort the geographic units by the health variable (e.g.,
infant mortality rate) from the worst to the best situation (highest to lowest
rate). The rates are then transformed into continuous variables and thecumulative
proportion is calculated for both variables. The graph showing the cumulative
proportion for the health variable (Y axis) and the cumulative proportion
of the population is then prepared, and the Gini coefficient can be calculated
as the absolute value of the result of the Brown formula.

Although the level of inequalities is reflected in the value of the Gini
coefficient itself (for example, a value very close to 0 will represent a
low level of inequality), the interpretation of the coefficient is usually
done in comparative terms, by contrasting the calculated value to that of
other geographic units, population groups etc. Again, a coefficient of 0.2
will represent a lower level of inequality than a coefficient of 0.4. The
cumulative proportions of borth variables can also be read directly from the
graphical representation of the Lorenz curve (see following example).

Concentration Index and Concentration Curve The socioeconomic dimension can be included in the analysis through the
calculation of the Concentration Index if the population or the geographic
units are ordered by socioeconomic status and not following a health variable.
The Concentration Index is calculated in the same way as the Gini Coefficient,
but it varies between –1 and +1. The values are negative when the curve is
above the diagonal and positive when they are under the curve. If the order
resulting from sorting by the socioeconomic and health variables are the same,
the concentration index will have the same absolute value as the Gini coefficient.

Following is an example of calculation of Gini Coefficient using infant mortality
rates from 5 countries of the Andean area in 1997 (PAHO, Basic indicators
brochure 1998). The data for this example are presented in table
1a and table 1b below. The Lorenz Curve is shown in
figure 2.

The steps for the calculation of the Gini coefficient and graphing of the
Lorenz curve are the following:

Sort the geographic units by the health variable (infant
mortality rate) from the worst situation (highest rate) to the best situation
(lowest rate).

Calculate the number of infant deaths for each geographic unit.

Calculate what proportion of the total of all infant deaths and what proportion
of the total of all live births is observed in each geographical unit.

Calculate the cumulative proportion of each of the two variables.

Calculate the Gini coefficient using the formula

Graph the curve using the X axis for the proportion of the cumulative population
(live births) and the Y axis for the proportion of cumulative health variable
observations (infant deaths).

Interpretation:
Gini Coefficient: In our example, the result was 0.20, which is not a
high value and is closer to zero (total equality) than 1 (total inequality).
However, to be able to have a complete picture of the situation, it would
be necessary to compare this value with the values obtained from the other
geographic areas.Lorenz Curve: For example, we read on the graph that 30% of infant
deaths occur among 20% of the population of live births.